{"id":"W2212962555","doi":"10.1111/bjet.12388","title":"<scp>VILLAGE</scp> — <scp>V</scp> irtual <scp>I</scp> mmersive <scp>L</scp> anguage <scp>L</scp> earning and <scp>G</scp> aming <scp>E</scp> nvironment: Immersion and presence","year":2015,"lang":"en","type":"article","venue":"British Journal of Educational Technology","topic":"Virtual Reality Applications and Impacts","field":"Computer Science","cited_by":116,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Chatbot; Affordance; Immersion (mathematics); Computer science; Virtual machine; Learning environment; Multimedia; Metaverse; Virtual learning environment; Language acquisition; Sense of presence; Human–computer interaction; World Wide Web; Virtual reality; Psychology; Mathematics education; Operating system","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts","scholarly_communication","open_science","research_integrity"],"consensus_categories":["metaepi_narrow","sts","research_integrity"],"category_scores_codex":[0.004496713,0.002463165,0.002972942,0.003451206,0.00271719,0.002650248,0.006212788,0.00253876,0.00002832182],"category_scores_gemma":[0.06424807,0.00290193,0.0009489506,0.004602957,0.00292376,0.005167478,0.003670635,0.005110983,0.0005269599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001383049,"about_ca_system_score_gemma":0.002636278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005236893,"about_ca_topic_score_gemma":0.000149524,"domain_scores_codex":[0.9819447,0.001222035,0.00401434,0.003996987,0.004027912,0.004794027],"domain_scores_gemma":[0.9632727,0.02320062,0.004890888,0.002811667,0.002290732,0.003533414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000122639,0.005002702,0.02288394,0.0007117266,0.002067015,0.001617118,0.04150887,0.002091461,0.02860183,0.02570181,0.8301474,0.03965382],"study_design_scores_gemma":[0.007719602,0.003701838,0.06365991,0.002438612,0.0007888578,0.02188355,0.1785149,0.004205351,0.01963852,0.02298555,0.6739566,0.0005067198],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9318116,0.03218259,0.01375421,0.001987844,0.002647356,0.002233503,0.0004658355,0.0005744165,0.01434267],"genre_scores_gemma":[0.9301477,0.01585465,0.01919753,0.002261042,0.002888189,0.000526621,0.0004976386,0.000590152,0.02803644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1561909,"threshold_uncertainty_score":0.9997897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01584549852487568,"score_gpt":0.2584738270067919,"score_spread":0.2426283284819163,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}